A system and method for adaptive cloud conversation platform

ABSTRACT

An adaptive cloud conversation platform capable of making automated decisions regarding when and how to establish on-going communications with consumers so as to maximize the relationship between the consumer and a given brand. The system has a connection management services layer which determines what communications should be established and how they should be established, an initiation management services layer which determines when communications should be established, and a user management services layer which stores information about consumers and brands for determination of when and how communications should be established. Certain of these services have machine learning algorithms incorporated into them trained to perform analyses of the particular type of operation handled by that service. The outputs of each service can be used as inputs to other services, such that a network of machine learnings algorithms is created which determines when and how to establish on-going communications with consumers.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed in the application data sheet to the followingpatents or patent applications, the entire written description of eachof which is expressly incorporated herein by reference in its entirety:

-   -   Ser. No. 17/893,006    -   Ser. No. 17/235,408    -   Ser. No. 17/358,331    -   Ser. No. 16/836,798    -   Ser. No. 16/591,096    -   Ser. No. 17/336,405    -   Ser. No. 17/011,248    -   Ser. No. 16/995,424    -   Ser. No. 16/896,108    -   Ser. No. 16/542,577    -   62/820,190    -   62/858,454    -   Ser. No. 16/152,403    -   Ser. No. 16/058,044    -   Ser. No. 14/532,001    -   Ser. No. 13/659,902    -   Ser. No. 13/479,870    -   Ser. No. 12/320,517    -   Ser. No. 13/446,758    -   Ser. No. 15/411,534    -   62/291,049

BACKGROUND OF THE INVENTION Field of the Art

The disclosure relates to the field of contact center technology,specifically to the field of cloud-implemented automated callbacksystems.

Discussion of the State of the Art

While various types of callback scheduling systems exist, they arelimited to basic scheduling functions such as queuing for callbacks bythe next available agent or callbacks based on consumer indications ofappropriate callback times. These systems can keep track of repeatedinteractions with consumers either by identifying the consumer's phonenumber or account, or by assigning repeated interactions to ticketnumbers specific to a particular reason for the interaction. However,other than tracking these repeated interactions so that agents can seethe history of interactions, these systems do not account for thecomplexity of on-going conversations with consumers and have no abilityto determine when and how to establish further communications withconsumers so as to maximize the relationship between the consumer and agiven brand.

What is needed is an adaptive cloud conversation platform capable ofautomated decisions regarding when and how to establish on-goingcommunications with consumers so as to maximize the relationship betweenthe consumer and a given brand.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived and reduced to practice, anadaptive cloud conversation platform capable of making automateddecisions regarding when and how to establish on-going communicationswith consumers so as to maximize the relationship between the consumerand a given brand. In an embodiment, the system comprises a connectionmanagement services layer which determines what communications should beestablished and how they should be established, an initiation managementservices layer which determines when communications should beestablished, and a user management services layer which storesinformation about consumers and brands for determination of when and howcommunications should be established. Certain of these services havemachine learning algorithms incorporated into them trained to performanalyses of the particular type of operation handled by that service.The outputs of each service can be used as inputs to other services,such that a network of machine learnings algorithms is created which,when operated together, determines when and how to establish on-goingcommunications with consumers so as to maximize the relationship betweenthe consumer and a given brand.

According to a preferred embodiment, an adaptive cloud conversationplatform is disclosed, comprising: a computing device comprising amemory, a processor, and a non-volatile data storage device; a consumerprofile database stored on the non-volatile data storage device, theconsumer profile database comprising one or more consumer profiles; asurvey manager comprising a first plurality of programming instructionsstored in the memory which, when operating on the processor, causes thecomputing device to: receive conversation data for a conversation with aconsumer, the conversation data comprising an interaction between theconsumer and a brand and satisfaction data for the conversation; processthe conversation data through a first machine learning algorithm toobtain a survey strategy, the survey strategy comprising a determinationthat a survey of the consumer should be conducted and a type of surveyto be conducted; and forward the survey strategy to a conversationmanager; a conversation manager comprising a second plurality ofprogramming instructions stored in the memory which, when operating onthe processor, causes the computing device to: receive the surveystrategy; retrieve a consumer profile for the consumer from the consumerprofile database, the consumer profile comprising a plurality ofpreferences of the consumer; process the plurality of preferencesthrough a second machine learning algorithm to select a channel throughwhich to conduct the survey with the consumer; and forward the channelselection to a schedule manager; a schedule manager comprising a thirdplurality of programming instructions stored in the memory which, whenoperating on the processor, causes the computing device to: receive thechannel selection from the conversation manager; retrieve the consumerprofile; process the plurality of preferences through a third machinelearning algorithm to select a time at which to conduct the callbackwith the consumer through the selected channel; schedule a survey to beconducted with the consumer at the selected time through the selectedchannel; and forward the survey schedule to a callback manager; and acallback manager comprising a fourth plurality of programminginstructions stored in the memory which, when operating on theprocessor, causes the computing device to: receive the survey schedule;and retrieve a survey of the type specified in the survey strategy;execute the survey strategy by conducting the survey at the selectedtime through the selected channel as indicated in the survey schedule.

According to another preferred embodiment, method for operating anadaptive cloud conversation platform, comprising the steps of: using asurvey manager operating on a computing device comprising a memory, aprocessor, and a non-volatile data storage device to: receiveconversation data for a conversation with a consumer, the conversationdata comprising an interaction between the consumer and a brand andsatisfaction data for the conversation; process the conversation datathrough a first machine learning algorithm to obtain a survey strategy,the survey strategy comprising a determination that a survey of theconsumer should be conducted and a type of survey to be conducted; andforward the survey strategy to a conversation manager; a conversationmanager comprising a second plurality of programming instructions storedin the memory which, when operating on the processor, causes thecomputing device to: receive the survey strategy; retrieve a consumerprofile for the consumer from a consumer profile database stored on thenon-volatile data storage device, the consumer profile databasecomprising one or more consumer profiles, and the consumer profilecomprising a plurality of preferences of the consumer; process theplurality of preferences through a second machine learning algorithm toselect a channel through which to conduct the survey with the consumer;and forward the channel selection to a schedule manager; using aschedule manager operating on the computing device to: receive thechannel selection from the conversation manager; retrieve the consumerprofile; process the plurality of preferences through a third machinelearning algorithm to select a time at which to conduct the callbackwith the consumer through the selected channel; schedule a survey to beconducted with the consumer at the selected time through the selectedchannel; and forward the survey schedule to a callback manager; andusing a callback manager operating on the computing device to: receivethe survey schedule; and retrieve a survey of the type specified in thesurvey strategy; execute the survey strategy by conducting the survey atthe selected time through the selected channel as indicated in thesurvey schedule.

According to an aspect of an embodiment, the survey manager is furtherconfigured to: receive survey feedback from the consumer; process thesurvey feedback through the first machine learning algorithm todetermine whether a callback is recommended based on the surveyfeedback; and forward the determination to the conversation manager; theconversation manager is further configured to: receive thedetermination; retrieve the consumer profile; process the plurality ofpreferences through the second machine learning algorithm to select achannel through which to conduct the callback with the consumer; theschedule manager is further configured to: receive the channel selectionfrom the conversation manager; retrieve the consumer profile; processthe plurality of preferences through the third machine learningalgorithm to select a time at which to conduct the callback with theconsumer through the selected channel; schedule a callback to beconducted with the consumer at the selected time through the selectedchannel; and forward the survey schedule to the callback manager; andthe callback manager is further configured to: receive the callbackschedule; and execute the callback by conducting the callback at theselected time through the selected channel as indicated in the callbackschedule.

According to an aspect of an embodiment, a consumer context manager isused to: receive the conversation data; retrieve the consumer profile,the consumer profile further comprising a plurality of behaviors of theconsumer; process the conversation data and the plurality of behaviorsof the consumer through a fourth machine learning algorithm to determinewhether a second callback to the consumer should be made; and where thedetermination is that a second callback should be made, forward thedetermination to the conversation manager as the determination that acallback should be made to a consumer.

According to an aspect of an embodiment, a session manager is used to:receive the conversation data; process the conversation data through afifth machine learning algorithm to determine a consumer sentiment; andforward the determined sentiment to the consumer context manager as anadditional input to the fourth machine learning algorithm'sdetermination as to whether a callback to the consumer should be made.

According to an aspect of an embodiment, the conversation manager isfurther configured to: process the conversation data through a sixthmachine learning algorithm to determine a consumer goal, need, orintent; and forward the determined goal, need, or intent to the consumercontext manager as an additional input to the fourth machine learningalgorithm's determination as to whether a callback to the consumershould be made.

According to an aspect of an embodiment, an event rules database isstored on the non-volatile data storage device, the event rules databasecomprising rules for triggering communications with consumers based onevents occurring outside of a conversation; and an event manager is usedto: receive notification of an event; match the event to a rule in theevent rules database; and forward the matched event to the conversationmanager as the determination that a callback should be made to theconsumer.

According to an aspect of an embodiment, the event manager is furtherconfigured to: retrieve the consumer profile; process the consumerprofile and one or more rules from the event rules database through aneighth machine learning algorithm to determine a new rule for triggeringcommunications with the consumer; and store the new rule in the eventrules database.

According to an aspect of an embodiment, a brand environment database isstored on the non-volatile data storage device, the brand environmentdatabase comprising brand information related to conversations withconsumers of the brand; and an environment manager is used to: retrievethe consumer profile; retrieve the brand information from the brandenvironment database; process the consumer profile and the brandinformation through a ninth machine learning algorithm to determinewhether a campaign of communications should be established with aplurality of consumers; and forward the determination to theconversation manager as the determination that a callback should be madeto the consumer.

According to an aspect of an embodiment, a consumer manager is used to:retrieve the consumer profile for the consumer from the consumer profiledatabase; receive a fitness parameter from the environment manager;process the consumer profile and the fitness parameter through a tenthmachine learning algorithm to identify opportunities for proactiveconversations with the consumer; and forward the identifiedopportunities to the consumer context manager as an additional input tothe second machine learning algorithm's selection of the channel throughwhich the callback should be made.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a block diagram illustrating an exemplary system architecturefor an adaptive cloud conversation platform connected to brand endpointscomprising contact centers.

FIG. 2 is a block diagram illustrating an exemplary system architecturefor an adaptive cloud conversation platform connected to brand endpointscomprising a cloud-based server with remote agents.

FIG. 3 is a block diagram illustrating an exemplary architecture for amultiple-instance adaptive cloud conversation platform connected tobrand endpoints comprising a cloud-based server with remote agents and abroker server intermediary.

FIG. 4 is a block diagram illustrating an exemplary system architecturefor a conversation manager aspect of an adaptive cloud conversationplatform.

FIG. 5 is a block diagram illustrating an exemplary system architecturefor a session manager aspect of an adaptive cloud conversation platform.

FIG. 6 is a block diagram illustrating an exemplary system architecturefor an event manager aspect of an adaptive cloud conversation platform.

FIG. 7 is a block diagram illustrating an exemplary system architecturefor a media server aspect of an adaptive cloud conversation platform.

FIG. 8 is a block diagram illustrating an exemplary system architecturefor a consumer manager aspect of an adaptive cloud conversationplatform.

FIG. 9 is a block diagram illustrating an exemplary system architecturefor a schedule manager aspect of an adaptive cloud conversationplatform.

FIG. 10 is a block diagram illustrating an exemplary system architecturefor an environment manager aspect of an adaptive cloud conversationplatform.

FIG. 11 is a block diagram illustrating an exemplary system architecturefor a machine learning algorithm network aspect of an adaptive cloudconversation platform.

FIG. 12 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for on-going sessionanalysis.

FIG. 13 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for callback mode analysis.

FIG. 14 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for proactive conversationanalysis.

FIG. 15 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for callback planning.

FIG. 16 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for event context analysis.

FIG. 17 is a flow diagram illustrating an exemplary use case examplesfor an adaptive cloud conversation platform.

FIG. 18 is a block diagram illustrating an exemplary system architecturefor an adaptive cloud conversation platform connected to brand endpointscomprising contact centers.

FIG. 19 is a block diagram illustrating an exemplary system architecturefor a survey manager aspect of an adaptive cloud conversation platform.

FIG. 20 is a block diagram illustrating an exemplary system architecturefor a machine learning algorithm network aspect of an adaptive cloudconversation platform.

FIG. 21 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for survey context manager.

FIG. 22 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 23 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 24 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 25 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, an adaptive cloudconversation platform capable of making automated decisions regardingwhen and how to establish on-going communications with consumers so asto maximize the relationship between the consumer and a given brand. Inan embodiment, the system comprises a connection management serviceslayer which determines what communications should be established and howthey should be established, an initiation management services layerwhich determines when communications should be established, and a usermanagement services layer which stores information about consumers andbrands for determination of when and how communications should beestablished. Certain of these services have machine learning algorithmsincorporated into them trained to perform analyses of the particulartype of operation handled by that service. The outputs of each servicecan be used as inputs to other services, such that a network of machinelearnings algorithms is created which, when operated together,determines when and how to establish on-going communications withconsumers so as to maximize the relationship between the consumer and agiven brand.

The platform is made adaptive through the use of one or more machinelearning algorithms, each trained to analyze data pertaining to aparticular set of aspects of a conversation and make recommendationswithin the context of those aspects. In certain embodiments, eachmachine learning algorithm is part of one or more platform components,and configured as a context manager for that component, receiving datapertaining to the particular set of aspects of a conversation for whichthat component is designed, and making recommendations within thecontext of that component. For example, a session manager component mayhave a machine learning algorithm which receives a real-time transcriptof an on-going audio conversation between a consumer and agent, andwhich proposes responses for the agent based on current consumerstatements, history of interactions with the consumer, consumer storedpreferences, databases of similar conversations with other consumers,etc. The outputs of the machine learning algorithm of one components maybe used an inputs to a machine learning algorithm of another component,creating a network of machine learning algorithms. In this way, thesystem adapts to conversations by listening to current conversations,learning from the current conversation and past conversations, changingits operation in response to changes in context or conditions, andadapting to new situations through the inherent predictive capabilitiesof machine learning algorithms. On shorter time scales, the platformadapts in response to incoming data by recommending proactiveconversation actions such as a current conversation or recentinteractions. On a longer time scale, the platform adapts (or evolves)by gradually incorporating newer information into its machine learningalgorithms by periodic or continuous re-training based on the newerinformation. The platform thus become smarter over time aboutconversations in general and about conversations with specific consumersin particular. These adaptations over short and long time scales abouthow to call, who to call, and when to call allow the platform to be“adaptive” of context and circumstances from the largest scales (allconsumers across all brands or large groups/representative averages,etc.), through mid-sized scales (certain types or groups of consumersacross brands, consumers for certain brands, certain types ofconversations with a certain consumer, etc.), to the smallest scales (acurrent conversation with a certain consumer, etc.). This “adaptiveness”provides a level of personalization and authenticity lacking in existingcustomer relationship management systems, and enables consumers to getthe help they need from the brands they love on the terms they choose.

Consumer surveys are an important part of managing relationships betweenbrands and their consumers. While consumer satisfaction can be inferredfrom interactions with consumers, receiving explicit feedback fromconsumers on their own time and in their preferred communicationchannels can be important for determining consumer satisfaction. Forexample, consumers who are hesitant to express dissatisfaction in anin-person communication with an agent such as a phone call may be morelikely to express that dissatisfaction in a more remote communicationsuch as a survey. Thus, surveys can lead to more honest feedback fromconsumers in certain situations.

However, surveys can also lead to exaggerations of satisfaction ordissatisfaction from consumers, as consumers may be more likely toprovide extreme feedback (e.g., one star out of five) in situationswhere there is not a live person on the other end of the communicationwho could be offended (such as an agent). So, it is useful to determinehow and when to conduct consumer surveys so as to maximize the honestyand accuracy of consumer feedback. Factors to be taken into account insuch determinations include, but are not limited to, the type of surveyto be conducted, the channels of communication through which the surveyis to be conducted, the consumer's preferences (e.g., channels, time ofday, days of the week, etc.), whether the survey has been triggered by aspecific event (e.g., the purchase of a product), and whether theconsumer has left previous feedback (especially where the feedback isrecent and negative).

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

“Callback” as used herein refers to contact by a brand to a consumerafter some interaction or attempted interaction between the consumer andthe brand. The term callback is not limited to telephone communications,and includes any form of communication whether in person or viaelectronic means such as, but not limited to, phone,voice-over-Internet-protocol (VOIP), email, short message service (SMS),and online messaging platforms.

“Consumer” as used herein means a potential buyer or consumer ofproducts and/or services. A consumer may be a person, group of persons,or a legal entity such as a company.

“Conversation” as used herein means a series of communications betweenat least one brand and at least one consumer. A conversation maycomprise many sessions, may use many different forms of communication(e.g., phone, email, SMS), and may span long periods of time. In somecases, conversations may be grouped into themes (e.g., a “ticket”related to a problem as used in some technical service systems), but theterm conversation as used herein is not so limited and may span anynumber of themes. A conversation involves communication between at leastone consumer and one brand, but is not limited to communications betweensingle brand and a single consumer, and may involve other brands orthird parties, as well. For example, in a real estate transaction, aconversation may involve two consumers (a buyer and a seller), as wellas multiple brands (the seller's agent or broker, the buyer's agent orbroker, a lender, a title company, an escrow company, etc.).

“Session” as used herein means a single communication between at leastone brand and at least one consumer. A session involves communicationbetween at least one consumer and one brand, but is not limited tocommunications between single brand and a single consumer, and mayinvolve other brands or third parties, as well. For example, in a realestate transaction, a session may involve two consumers (a buyer and aseller), as well as multiple brands (the seller's agent or broker, thebuyer's agent or broker, a lender, a title company, an escrow company,etc.).

Conceptual Architecture

FIG. 1 is a block diagram illustrating an exemplary system architecturefor an adaptive cloud conversation platform connected to brand endpointscomprising contact centers. The adaptive cloud conversation platform(ACCP) 100 is designed to provide automated,machine-learning-algorithm-supported complex conversation support tobrands to establish, enhance, and maintain relationships with theirconsumers. Its capabilities exceed those of traditional callback systemsor customer relationship management systems in that it is designed notjust to keep records of past interactions between a given brand and agiven consumer and schedule callbacks from the brand to the consumer,but to manage the entirety of complex conversations that may occurbetween consumers and brands, including conversations involving multipleconsumers and multiple brands.

The adaptive cloud conversation platform 100, therefore, manages complexconversations, possibly over an extended period of time, between one ormore brand endpoints 150 of a brand and one or more consumer endpoints140 of a consumer or consumers. The consumer endpoints 140 may be anydevice used by a consumer for communications including, but not limitedto plain old telephone service (POTS) 142, mobile phones or smartphones143, tablet computers 144, laptop computers 145, and desktop computers146. While not shown here, in-person communications (without devices) isalso considered to be a consumer endpoint 140 (e.g., in some cases, theadaptive cloud conversation platform 100 may recommend in-personcommunications for certain sessions). Consumer-side communicationchannels 141 between the consumer endpoints 140 and the adaptive cloudconversation platform 100 may be established or triggered through anymeans supported by the consumer endpoints 140 including, but not limitedto, voice, virtual assistants (VAs), chatbots, web sessions, electronicbeacons placed in physical locations such as store displays, andgeofenced triggers.

The brand endpoints 140 may be any device used by a consumer forcommunications including, but not limited to plain old telephone service(POTS) 161, mobile phones or smartphones 162, tablet computers 163,laptop computers 164, and desktop computers 165. While not shown here,in-person communications (without devices) is also considered to be abrand endpoint 140 (e.g., in some cases, the adaptive cloud conversationplatform 100 may receive data from an in-person purchase at abrand-owned store). In this embodiment, the brand endpoints 150 arelocated at a contact center 160 which handles consumer relations for thebrand, either as a brand-owned contact center or a third party contactcenter service. Further, the brand endpoints in some cases may beconversation bots 170 (also known as chatbots or interactive voiceresponse (IVR) systems) instead of human agents. Brand-sidecommunication channels 151 between the brand endpoints 150 and theadaptive cloud conversation platform 100 may be established or triggeredthrough any means supported by the brand endpoints 150 including, butnot limited to, voice, virtual assistants (VAs), chatbots, web sessions,electronic beacons placed in physical locations such as store displays,and geofenced triggers.

In this embodiment, the system can be conceived of as comprising aconversation manager 400, a connection management services layer 110, aninitiation management services layer 120, and a user management serviceslayer 130. However, certain components within each of these layers mayperform some aspects of other layers, so there can be cross-over betweenlayers in some cases. Further, in other embodiments, the functionalitiesof certain components described herein may be performed by othercomponents, depending on platform configuration.

The conversation manager 400 is the component that determines the scopeof each conversation and manages overall communications between thecomponents at each layer accordingly, although data may still be passeddirectly from component to component. The connection management serviceslayer 110 generally determines what communications should be establishedand how they should be established (often referred to herein as a “mode”of communications). The connection management services layer 110comprises a media server 700 which establishes communications betweenconsumer endpoints 140 and brand endpoints 150 and performs anynecessary media translations (e.g., automated speech recognition, textto speech, etc.), a session manager 500 which assigns sessionidentifiers, instructs the media server 700 to establish connections,and handles all incoming, outgoing, and stored data associated with eachsession, and a callback manager 111 which executes callbacks initiatedby the initiation management services layer 120 by instructing thesession manager 500 to initiate a callback.

The initiation management services layer 120 determines whencommunications should be established. The initiation management serviceslayer 120 comprises an event manager 600 which initiates callbacks basedon events occurring outside of a session, and a schedule manager 900which determines when communications should be established and schedulescallbacks based on the determinations. Callbacks initiated by the eventmanager 600 and schedule manager 900 are passed up to the connectionmanagement service layer 110 and executed by the callback manager 111.

The user management services layer 130 stores information aboutconsumers and brands for determination of when and how communicationsshould be established. The user management services layer 130 comprisesa consumer manager 800 which manages the profiles of consumers for usein establishing effective conversations with them, and an environmentmanager 1000 which stores information about brands including operationsinformation and analysis, locations and status of communicationsinfrastructure, and customer relationship management (CRM) information,plus and event thrower which can trigger scheduling of callbacks bysending instructions up to the initiation management services layer 120for event rule establishment by the event manager 600 or scheduling bythe schedule manager 900.

Certain of these services have machine learning algorithms incorporatedinto them trained to perform analyses of the particular type ofoperation handled by that service. The outputs of each service can beused as inputs to other services, such that a network of machinelearnings algorithms is created which, when operated together,determines when and how to establish on-going communications withconsumers so as to maximize the relationship between the consumer and agiven brand.

FIG. 2 is a block diagram illustrating an exemplary system architecturefor an adaptive cloud conversation platform connected to brand endpointscomprising a cloud-based server with remote agents. In this embodiment,the adaptive cloud conversation platform 100 is the same as describedabove in FIG. 1 , but the interface with the brand endpoints isdifferent. In this embodiment, the brand endpoints 150 are remote agentsmanaged through a cloud-based server 260 instead of being on-site agentsat a contact center 160.

FIG. 3 is a block diagram illustrating an exemplary architecture for amultiple-instance adaptive cloud conversation platform connected tobrand endpoints comprising a cloud-based server with remote agents and abroker server intermediary. In this embodiment, a plurality of adaptivecloud conversation platforms (ACCPs) 100 a-n, each having capabilitiesthe same as or similar to that described in FIG. 1 , are managed by abroker server 180. In this embodiment, the broker server 180 mediatesbetween brands and adaptive cloud conversation platform instances 100a-n. Depending on configuration, this embodiment supports a federatedconversation arrangement where a consumer can interact with multiplebrands through a single ACCP, or where one brand can use different ACCPinstances to handle callbacks, or a combination of the two.

FIG. 4 is a block diagram illustrating an exemplary system architecturefor a conversation manager aspect of an adaptive cloud conversationplatform. The conversation manager 400 is responsible for determiningthe scope of conversations and for overall coordination of conversationswith other components of the platform based on the scope. In thisembodiment, the conversation manager 400 comprises a conversation scopemanager 410, a conversation context manager 420, a conversation scriptmanager 430, and a conversation management database 440.

The conversation scope manager 410 is responsible for determining andmanaging the scope of conversations. The conversation scope managerinterfaces with other components of the system to either receiveinformation about current or scheduled conversations or to directinitiation of conversations. In this embodiment, the conversation scopemanager interfaces with the event manager to establish rules fortriggering events and to receive notification of events that have beentriggered, the schedule manager to schedule callbacks and receivenotification of callbacks due for initiation, the session manager toinitiate callbacks and receive data regarding on-going callbacks, theconsumer manager to store and retrieve consumer profile data, and theenvironment manager to receive brand-established rules, fitnessparameters, and other brand-related information. As conversations mayinvolve multiple themes, extend over many sessions and over extendedperiods of time, and may involve more than one consumer and/or more thanone brand, the conversation scope manager 410 is responsible fordetermining the scope of a given conversation through the use of globaland local variables. For example, consumers may be assigned a globalvariable such that they are recognized throughout all components of theplatform and at all stages. However, local scope variables may beassigned for each conversation, session, event, brand campaign, etc., todecrease dependencies within the platform, to separate conversations, todecrease the likelihood of data corruption, and to reduce overallcomplexity of the platform. For example, if a conversation startsbetween a buyer's real estate agent (brand) and the buyer (consumer), ascope will be assigned to that conversation. If and when theconversation extends to include other parties such as a seller's realestate agent (brand) and the seller, the seller's agent and seller maybe recognized by the platform due to their global scope, but will beincorporated into the conversation's local scope for purposes ofnegotiating and completing that particular real estate transaction.Other scope variables may be assigned, such as session scope variablesfor communications between parties within the overall scope of theconversation about the real estate transaction. The conversation scopemanager 410 keeps track of conversations, sessions, participants, andscopes, and stores them in the conversation management database 440 forlater use.

The conversation context manager 420 is responsible for analyzing thecontext of conversations and, in particular, for determining the goals,needs, or intents of consumers for the purpose of recommending an action(e.g., scheduling a callback, changing modes of conversation, etc.). Theconversation context manager incorporates a trained machine learningalgorithm which receives conversation related data and determines agoal, need, or intent of the consumer using a goals and intentsidentifier 421 and recommends a corresponding action using an actiongenerator 422. The training and operation of the machine learningalgorithm is described later herein. The output of the conversationcontext manager 420 may be fed to other components for actions to betaken (e.g., a recommendation to schedule a callback by phone with aparticular agent may be sent to the schedule manager to schedule thecallback) or may be fed to the machine learning algorithms of othercomponents for further analysis (e.g., a determination that a consumeris dissatisfied with the current line of discussions with an agent maybe sent to the session context manager 520 for proposal of a differentset of responses). In this way, the outputs of the machine learningalgorithm(s) of each component of the platform may be acted onseparately, or may be used as part of a network of machine learningalgorithms, or some combination of the two.

The conversation script manager 430 contains a library of conversationscripts against which transcripts of conversations may be compared foranalysis purposes or from which conversation scripts can be drawn inorder to respond to on-going sessions. Conversation scripts may be inthe form of relational databases which associate consumer queries withagent (or chatbot) responses, and may further include associations withcontext such as the goals, needs, and intents of a consumer, which maybe used with the outputs of the conversation context manager 420 toprovide appropriate responses to consumer queries.

FIG. 5 is a block diagram illustrating an exemplary system architecturefor a session manager aspect of an adaptive cloud conversation platform.The session manager is responsible for handling of communicationsessions including assigning of session identifiers, instructing themedia server 700 to establish connections, and handling all incoming,outgoing, and stored data associated with each session. In thisembodiment, the session manager comprises a session scope manager 510, asession context manager 520, a session state manager 530, a sessionreceiver 540, and a session initiator 550.

The session scope manager 510 is responsible for implementing thesession within its defined scope as determined by the conversation scopemanager 510. In the case where the conversation manager 400 directs thesession scope manager 510 to initiate communications with a consumer,the directions from the conversation manager 400 will contain thesession scope. In the event that contact is initiated by a consumer, thesession scope manager 510 will send session data to the conversationmanager 400 for determination of the session scope. For example, thesession scope may be a phone call between a buyer and a buyer's agent inthe context of a larger conversation about a real estate transaction.

The session context manager 520 is responsible for analyzing the contextof conversations and, in particular, for determining the sentiment of aconsumer during the session using text of the session provided by themedia server 700. The session context manager 520 incorporates a trainedmachine learning algorithm which receives session related data anddetermines a sentiment of the consumer using a sentiment analyzer 521and recommends a corresponding action using an action generator 522. Thetraining and operation of the machine learning algorithm is describedlater herein. The output of the session context manager 520 may be fedto other components for actions to be taken (e.g., a recommendation toescalate a conversation by switching modes from chat to a voice-basedphone call) or may be fed to the machine learning algorithms of othercomponents for further analysis (e.g., a determination that a consumeris satisfied with the current line of discussions with the current call,and may be receptive to additional offers, which action may be sent tothe environment manager 1000 for consideration of follow up offers). Inthis way, the outputs of the machine learning algorithm(s) of eachcomponent of the platform may be acted on separately, or may be used aspart of a network of machine learning algorithms, or some combination ofthe two.

The session state manager 530 stores state information such as thecurrent session scope, whether the session is on-going or completed,what type of connection(s) are being used at the consumer endpoints 140and brand endpoints 150, the contact information used to establish theconnection(s), etc. When a session is completed, the data stored in thesession state manager 530 is sent to the conversation manager 400 forstorage within the overall conversation (e.g., updates to the consumerprofile, the brand CRM information, etc.).

The session receiver 540 receives communications initiated by theconsumer and initiates a session by notifying the session scope manager510. For example, if a consumer makes a phone call to a brand, thesession receiver receives data about the phone call (e.g., consumer'sphone number) from the media server, and notifies the session scopemanager 510 which coordinates with the session scope with theconversation manager 400.

The session initiator 550 receives directions from the session scopemanager 510 to initiate a session, and directs the media server 700 tomake the appropriate communiation connections.

FIG. 6 is a block diagram illustrating an exemplary system architecturefor an event manager aspect of an adaptive cloud conversation platform.The event manager 600 initiates callbacks based on events occurringoutside of a session based on established event rules (e.g., callbackson a consumer's birthday; promotional events established by a brand) orexternal events (e.g., a consumer's purchase of a product from a brand;weather events in the consumer's area that might suggest purchase of aproduct such as snow tires prior to a snow storm). Callbacks initiatedby the event manager 600 are passed up to the connection managementservice layer 110 and executed by the callback manager 111. In thisembodiment, the event manager 600 comprises an event handler 610, anevent context manager 620, an event script database 630, an event rulesdatabase 640, and an event thrower 650.

The event handler 610 is responsible for triggering communicationsessions based on events outside of a current session based onestablished event rules or external events. It does so by monitoringevent notifications received from other components in the system, fromoutside sources (e.g., news websites, weather websites, etc.), or fromschedules to determine whether any of the event notifications trigger arule stored in the event rules database 640. Such rules may be simple(e.g., contact a consumer on his/her birthday) or complex (e.g., offersnow tires to consumers living in a certain geographical area during thewinter season when weather reports indicate snow in that geographicalarea within the next week). When a rule is triggered by an eventnotification, a corresponding event script is retrieved from an eventscript database 630, the event script providing instructions forhandling of the event (e.g., send out a broadcast email to all affectedconsumers), which instructions are sent to an event thrower 650 whichimplements the script by throwing an event with an event action to thecommunication manager 400.

The event context manager 620 analyzes established rules and externalevents to identify additional relationships between the rules, externalevents, and consumers. The event context manager 620 incorporates atrained machine learning algorithm which receives conversation relateddata and identifies additional relationships (possibly unknown orhidden) using a rules analyzer 621 and an external events analyzer 622,and recommends a corresponding action using an action generator 322. Thetraining and operation of the machine learning algorithm is describedlater herein. The output of the event context manager 620 may be fed toother components for actions to be taken (e.g., a recommendation offerother winter-related products to certain consumers in addition to snowtires) or may be fed to the machine learning algorithms of othercomponents for further analysis (e.g., forwarding of the identifiedadditional relationships to the environment manager 1000 for analysis).In this way, the outputs of the machine learning algorithm(s) of eachcomponent of the platform may be acted on separately, or may be used aspart of a network of machine learning algorithms, or some combination ofthe two.

FIG. 7 is a block diagram illustrating an exemplary system architecturefor a media server aspect of an adaptive cloud conversation platform.The media server 700 establishes communications between consumerendpoints 140 and brand endpoints 150 and performs any necessary mediatranslations (e.g., automated speech recognition, text to speech, etc.).In this embodiment, the media server 700 comprises communicationsinterfaces 710, a text-to-speech engine 720, an automated speechrecognition engine 730, and a bot interface manager 740.

The communications interfaces 710 comprise communications channels onboth the consumer side 711 a-n and the brand endpoint side 712 a-n,including, but not limited to text channels 711 a, 711 b for chatwindows and short message service (SMS) messages, email channels 711 b,712 b for sending and receipt of commuications via email, phone channels711 c, 712 c for communications via plain old telephone service (POTS)lines, and voice-over-Internet-protocol (VOIP) channels 711 n, 712 n forvoice communications over the Internet. Note that the communicationsinterfaces 710 may also contain hardware and software for conversion ofone type of channel on one end to a different type of channel on theother end. For example, the audio of a call from a consumer who callsusing a POTS phone channel 711 c may be converted to a VOIP channel 712n on the brand end, and vice-versa, so as to facilitate communicationsbetween different channels on either end. In some cases, text-basedchannels 711 a-b, 712 a-b may be converted to audio channels 711 c-n,712 c-n and vice-versa by using the automated speech recognition (ASR)engine 730 to convert spoken audio to text and by using thetext-to-speech (TTS) engine 720 to convert text to spoken audio.

The text-to-speech engine 720 may further be used to convert spokenaudio to text for analysis by other components such as the sessionmanager 500 or the conversation manager 400. This may be done innear-real-time or may be done with a delay such as by recording theaudio and converting the recorded audio at a later time.

The bot interface manager 740 is used to relay communications from thebrand-side communications interfaces 712 a, b to an appropriateconversation bot (e.g., a text-based chatbot or to an appropriateaudio-based interactive voice response (IVR) system or virtual assistantsuch as Siri, Alexa, or similar). Where the conversation bot is a thirdparty conversation bot such as Siri or Alexa, the bot interface manager740 also forwards the consumer-bot communications the brand forintegration into its CRM database and other systems, or for handling byan agent at a brand endpoint 150 if the call is later transferred tosuch agent.

FIG. 8 is a block diagram illustrating an exemplary system architecturefor a consumer manager aspect of an adaptive cloud conversationplatform. The consumer manager 800 manages the profiles of consumers foruse in establishing effective conversations with them, and engages inproactive conversation analysis to determine whether a proactivecommunication with a consumer should be initiated. In this embodiment,the consumer manager 800 comprises a profile manager 810, a consumerprofile database 820, and a consumer context manager 830.

The profile manager 810 handles all consumer data updates receivedeither from the consumer or from other components of the system, andstores and retrieves information from consumer profiles in the consumerprofile database 820 and forwards this information to other platformcomponents, as needed. Consumer profiles may contain any informationrelevant to a consumer, his or her relationships with one or morebrands, and any conversations in which the consumer has taken part. Thisincludes, but is not limited to, the name, address, phone number, andother identifying information of the consumer; brands with whom theconsumer has interacted and histories of such interactions, includingproduct/service inquiries, product/service purchases and returns, andproduct/service reviews and comments; and logs and recordings ofconversations, analyses of such conversations to determine wants, needs,intents, and goals, and lists of other consumers with whom the consumerhas had conversations and their relationships. In some embodiments, eachconsumer will have a single, unique profile for all conversations andall brands. In other embodiments, more than one profile may beestablished for a given consumer, depending on the configuration (e.g.,a profile may be established for each brand with whom the consumer hashad one or more conversations). The profile manager 810 notifies theevent manager 810 of changes to the state of the profile (e.g., a changein a consumer's address) that may impact event rules.

The consumer context manager 830 learns about the consumer's behaviorsand preferences in relation to events and brand attributes that areassociated with consumer behaviors. Consumer behaviors and preferencesinclude, but are not limited to, types and amounts of products andservices purchased, dates and times of purchases, types and amounts ofentertainment media consumed (e.g., computer games, television crimeseries, documentaries), interests indicated by certain purchases (e.g.,purchases of season tickets to a baseball stadium indicate an interestin sports, especially baseball). Consumer behaviors and preferences maybe implied (e.g., the purchase of baseball tickets implies an interestin baseball) or expressed (e.g., “I like watching baseball.”). Theconsumer behaviors and preferences are likely to be associated withcertain events and/or brand attributes. The consumer context manager 830incorporates a trained machine learning algorithm which recommendsconversations with consumers based on predicted associations betweenconsumer behaviors and preferences and events and/or brand attributes.The training and operation of the machine learning algorithm isdescribed later herein. The output of the consumer context manager 830may be fed to other components for actions to be taken (e.g., schedulingof a sales call based on an upcoming event to which the consumer may beresponsive) or may be fed to the machine learning algorithms of othercomponents for further analysis (e.g., to the conversation manager todetermine the best mode of starting the a conversation based on theupcoming event). In this way, the outputs of the machine learningalgorithm(s) of each component of the platform may be acted onseparately, or may be used as part of a network of machine learningalgorithms, or some combination of the two.

FIG. 9 is a block diagram illustrating an exemplary system architecturefor a schedule manager aspect of an adaptive cloud conversationplatform. The schedule manager 900 determines when communications shouldbe established and schedules callbacks based on the determinations. Inthis embodiment, the schedule manager 900 comprises a callback scheduler910, a callback planner 911, a rules scheduler 920, an agent scheduler930, and a schedule forecaster.

The callback scheduler 910 receives requests to schedule callbacks fromother platform components and determines when the callback should bemade based on consumer preferences and availability from the consumerprofile, rules for scheduling callbacks set forth in the rules scheduler920, agent availabilities set forth in an agent scheduler 930, andforecasts made by the schedule forecaster 940 of when the consumer andagent will be mutually available if and when certain rules are active.The determination of when the callback should be made is performed bythe callback planner 911. The callback planner 911 is responsible foranalyzing the data set forth above and determining when to schedule thecall. The callback planner 911 incorporates a trained machine learningalgorithm which receives the above data and predicts one or morepreferred callback times, which are then placed into the schedule by thecallback scheduler 910. The training and operation of the machinelearning algorithm is described later herein. The output of the callbackplanner 911 may be fed to other components for actions to be taken(e.g., a change in event rules of the event manager 600) or may be fedto the machine learning algorithms of other components for furtheranalysis although, in this embodiment, the callback scheduler is thefinal destination of the determinations of all other machine learningalgorithms. In this way, the outputs of the machine learningalgorithm(s) of each component of the platform may be acted onseparately, or may be used as part of a network of machine learningalgorithms, or some combination of the two.

FIG. 10 is a block diagram illustrating an exemplary system architecturefor an environment manager aspect of an adaptive cloud conversationplatform. The environment manager 1000 stores information about brandsincluding operations information and analysis, locations and status ofcommunications infrastructure, and customer relationship management(CRM) information, which can be used to trigger scheduling of callbacksby sending instructions up to the initiation management services layer120 for event rule establishment by the event manager 600 or schedulingby the schedule manager 900. In this embodiment, the environment manager1000 comprises an operations manager 1010, an environment contextmanager 1020, a customer relationship management (CRM) database 1030, anevent thrower 1040, and a brand environment database 1050.

The operations manager 1010 contains an interface through which brandscan enter or upload their brand environment data and customerrelationship management (CRM) data. The brand environment data isinformation about the brand useful for management of conversations withconsumers and may include, but is not limited to, infrastructure data(e.g., sales locations; contact center locations, capacities, andcapabilities, etc.), operational data (e.g., current and scheduled callvolumes, etc.), product/service information, fitness parameters (e.g.,average 90% retention rate for consumers), all of which is stored in thebrand environment database 1050. The CRM data is information about theconsumers and their contacts with the brand and includes, but is notlimited to, consumer identifications and contact information, historiesof interactions with the brand, and products and services purchased, allof which is stored in the CRM database 1030. Note that the informationstored in the CRM database 1030 may be exported to and duplicated in theconsumer profile database 820.

The environment context manager 1020 is responsible for analyzing theinformation contained in the brand environment database 1050 and CRMdatabase 1030 to determine whether a campaign of communications shouldbe established with a plurality of consumers. The environment contextmanager 1020 incorporates a trained machine learning algorithm whichanalyzes the information brand environment database 1050 and CRMdatabase 1030 using an environment analyzer 1021, further determineswhether there are appropriate operational resources (e.g., agents withappropriate skills, sufficient contact center capacity given current orpredicted call volumes, etc.) using an operations analyzer 1022, andrecommends a corresponding action using an action generator 1023. Thetraining and operation of the machine learning algorithm is describedlater herein. The output of the environment context manager 1020 may befed to other components for actions to be taken (e.g., a recommendationto schedule a callbacks by phone with a plurality of consumers may besent to the schedule manager to schedule the callbacks) or may be fed tothe machine learning algorithms of other components for further analysis(e.g., a request to determine a best mode of communication may be sentto the conversation manager for analysis). In this way, the outputs ofthe machine learning algorithm(s) of each component of the platform maybe acted on separately, or may be used as part of a network of machinelearning algorithms, or some combination of the two.

The event thrower 1040 throws events to other platform components withinstructions to schedule or establish communications with a consumereither associated with an existing conversation or a new conversation.

FIG. 11 is a block diagram illustrating an exemplary system architecturefor a machine learning algorithm network aspect of an adaptive cloudconversation platform. This diagram shows the machine learning algorithm(MLA) of each platform component and an exemplary configuration of suchMLAs into a network of MLAs for complex decision-making related toconversations. In this embodiment, the MLA network comprises the sessioncontext manager 520, the conversation context manager 420, theenvironment analyzer 1050, the event context manager 620, the callbackplanner 911, and the consumer context manager, arranged in a networksuch that the outputs of certain MLAs can be used as inputs to otherMLAs to make more complex or refined decisions than are possible using asingle MLA. The components of the MLA network are arranged conceptuallyinto three overall types of analysis. On-going session analyses 1110(i.e., what is happening in the current communication) are performed bythe session context manager 520. Callback mode analyses 1120 (i.e., howa call should be made, what channels, etc.) are performed by theconversation context manager 420 and/or the environment analyzer 1050.Callback timing analyses 1130 (i.e.g, when a call should be made) areperformed by the event context manager 620, the callback planner 911,and the consumer context manager 830.

In this embodiment, the session context manager 520 receives real-timeor near-real-time text data from the media server for any on-goingcalls, processes the data through one or more machine learningalgorithms to perform sentiment analysis and then propose responses tothe consumer based on the sentiment analysis. In some embodiments, theproposed responses may be generated from unsupervised machine learningalgorithms or reinforcement machine learning algorithms rather thansupervised (i.e., pre-trained) machine learning algorithms. Anunsupervised machine learning algorithm learns from the data itself byassociation, clustering, or dimensionality reduction, rather than havingbeen pre-trained to discriminate between labeled input data.Reinforcement learning algorithms learn from repeated iterations ofoutcomes based on probabilities with successful outcomes being rewarded.These types of machine learning algorithms are ideal for exploring largenumber of possible outcomes such as possible outcomes from differentapproaches to a conversation, and so would be suitable for proposingresponses to consumer queries.

In this embodiment, the sentiment analysis and proposed responses may beprovided to the conversation context manager 420 for performance of agoals, needs, and intents analysis. For example, the session contextmanager 520 determines that the consumer's sentiment on the call isnegative, the text data and the indication of negative sentiment may beanalyzed by the conversation context manager 420 which may determinethat the consumer actually wants to buy a replacement product ratherthan fix the existing one. This intent analysis can then be sent back tothe session manager for proposal of an appropriate responses.Alternately, the intent analysis can be sent to the callback planner 911for scheduling of a callback from a salesperson who can sell thereplacement product instead of the current agent trying to fix theproduct.

In this embodiment, the environment context manager 1020 may likewisedetermine whether and how to establish a conversation with a consumer byanalyzing the information contained in the brand environment database1020 and CRM database 1030 to determine whether a communication shouldbe scheduled or established with a consumer based on data within thebrand's environment. The outputs of the environment context manager 1020may be sent to a consumer context manager 830 for determination as towhether a proactive conversation should be started based on thecombination of environment context manager 1020 outputs and theinformation in the consumer profile database such as conversationhistories. If a conversation is recommended either by the environmentcontext manager 1020 or the consumer context manager 830, therecommendation is sent either to the conversation context manager 420for determination of a best mode of communication or, if the mode isknown or not of particular importance, to the callback planner 911 fordetermination of a best communication time.

In this embodiment, the consumer context manager 830 performs proactiveconversation analysis based on consumer behaviors, consumer preferences,events associated with consumer behaviors and/or preferences, and brandattributes associated with consumer behaviors and/or preferences todetermine whether a proactive conversation should be initiated with aconsumer. The outputs of the consumer context manager 830 may be sent tothe callback planner 910 for scheduling, or to the conversation contextmanager 420 for determination of a best mode of for initiating aproactive conversation.

Finally, in this embodiment, the event context manager's 620 machinelearning algorithms may determine that a communication should beestablished based on a rule, external event, or analysis of rules orexternal events combined with information stored in the consumer profiledatabase. In such a case, the outputs of the event context manager 620are sent to the callback planner 911 for determination of a bestcommunication time.

Detailed Description of Exemplary Aspects

FIG. 12 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for on-going sessionanalysis. In this example, it is assumed that a supervised machinelearning algorithm (MLA) is being used, but in other embodiments,unsupervised machine learning algorithms or reinforcement machinelearning algorithms may be used if better suited to the analyses beingperformed. Here, the machine learning algorithm is trained 1210 onlabeled data such as sentiment libraries 1211 a, indications of consumerwants/needs in relation to certain words and phrases 1211 b, agentskills 1211 c, and interaction outcomes based on the above 1211 n. Oncethe MLA has been trained, actual data is processed 1220 through thetrained machine learning algorithm such as a statement from a real-timeconversation transcript from the media server 1221 a, consumer profiledata 1221 b, and brand/agent data 1221 n. The MLA outputs a predictionregarding the best response 1230 to the consumer's statement, and anaction is taken based on the prediction 1240 such as having the agentuse the preferred response 1241 a, escalating the call to a manager 1241b, or sending the data to the conversation context manager for a goals,needs, and intents analysis 1241 n to better determine a response. Asfeedback data is received from the callback 1250, it may be used toretrain the machine learning algorithm 1260. Over time, the MLA willadapt its outputs to the retraining based on real-world data to providemore accurate predictions.

FIG. 13 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for callback mode analysis.In this example, it is assumed that a supervised machine learningalgorithm (MLA) is being used, but in other embodiments, unsupervisedmachine learning algorithms or reinforcement machine learning algorithmsmay be used if better suited to the analyses being performed. Here, themachine learning algorithm is trained 1310 on labeled data such assession context manager outputs 1311 a, indications of consumerwants/needs in relation to certain words and phrases 1311 b, agentskills 1311 c, fitness parameters established by the brand 1311 d, andinteraction outcomes based on the above 1311 n. Once the MLA has beentrained, actual data is processed 1320 through the trained machinelearning algorithm such as session context manager outputs 1321 a,consumer profile data 1321 b, and brand/agent data 1321 n. The MLAoutputs a prediction regarding the best mode of communication 1330 withthe consumer, and an action is taken based on the prediction 1340 suchas changing the approach to the conversation 1341 a, changing thechannel of communication 1341 b, changing agents 1341 c, or sending thedata to the callback planner to schedule a callback 1341 n to betterdetermine a response. As feedback data is received from the callback1350, it may be used to retrain the machine learning algorithm 1360.Over time, the MLA will adapt its outputs to the retraining based onreal-world data to provide more accurate predictions.

FIG. 14 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for proactive conversationanalysis. In this example, it is assumed that a supervised machinelearning algorithm (MLA) is being used, but in other embodiments,unsupervised machine learning algorithms or reinforcement machinelearning algorithms may be used if better suited to the analyses beingperformed. Here, the machine learning algorithm is trained 1410 onlabeled data such as consumer behaviors 1411 a, consumer preferences1411 b, events associated with consumer behaviors and/or preferences1411 c, and brand attributes associated with consumer behaviors and/orpreferences 1411 n. Once the MLA has been trained, actual data isprocessed 1420 through the trained machine learning algorithm such asconsumer profile data 1421 a, and event data 1421 b, and brand attributedata 1421 n. The MLA outputs a recommendation regarding a proactiveconversation 1430 with the consumer, and an action is taken based on theprediction 1440 such as sending the data to the callback planner toschedule a conversation 1441 a or sending the data to the conversationcontext manager 1441 n to select a best mode for initiating aconversation. As feedback data is received from the callback 1450, itmay be used to retrain the machine learning algorithm 1460. Over time,the MLA will adapt its outputs to the retraining based on real-worlddata to provide more accurate predictions.

FIG. 15 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for callback planning. Inthis example, it is assumed that a supervised machine learning algorithm(MLA) is being used, but in other embodiments, unsupervised machinelearning algorithms or reinforcement machine learning algorithms may beused if better suited to the analyses being performed. Here, the machinelearning algorithm is trained 1510 on labeled data such as consumer modepreferences 1511 b, consumer availabilities 1511 c, agent skills 1511 d,and agent availabilities 1511 n. Once the MLA has been trained, actualdata is processed 1520 through the trained machine learning algorithmsuch as proactive conversation analysis outputs 1521 a, conversationcontext manager outputs 1521 b, and event context manager outputs 1521n. The MLA outputs a recommendation regarding a callback time 1530 withthe consumer, and the recommendation is sent to the callback schedulerfor scheduling 1540. As feedback data is received from the callback1550, it may be used to retrain the machine learning algorithm 1560.Over time, the MLA will adapt its outputs to the retraining based onreal-world data to provide more accurate predictions.

FIG. 16 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for event context analysis.In this example, it is assumed that a supervised machine learningalgorithm (MLA) is being used, but in other embodiments, unsupervisedmachine learning algorithms or reinforcement machine learning algorithmsmay be used if better suited to the analyses being performed. Here, themachine learning algorithm is trained 1610 on labeled data such astriggering events 1611 b, consumer profile information 1611 c, agentskills 1611 d, and interaction outcomes from the above 1611 n. Once theMLA has been trained, actual data is processed 1620 through the trainedmachine learning algorithm such as triggering events 1621 a from theevent manager, consumer profile data 1621 b, and available agent skills1611 n. The MLA outputs a recommendation for a callback based on thetriggering event 1630, and the recommendation is sent to the callbackscheduler to schedule a callback 1640. As feedback data is received fromthe callback 1650, it may be used to retrain the machine learningalgorithm 1660. Over time, the MLA will adapt its outputs to theretraining based on real-world data to provide more accuratepredictions.

FIG. 17 is a flow diagram illustrating an exemplary use case examplesfor a mindful cloud conversation platform. In a standard callbackscenario 1710, a consumer may request a callback based on a failed ordelayed contact with a brand 1711 (e.g., being put on hold prior tospeaking with an agent), and a callback is made either by the firstavailable agent or according to another rule 1712. In a proactiveconversation scenario 1720, the consumer may interact with the brand insome way (e.g., by making a purchase) that is entered into the brandenvironment database. A determination is made that a callback iswarranted or would be useful as some future time (e.g., survey on thefollowing day, survey after delivery of the product purchased) 1721. Ina media-switching callback scenario 1730, a consumer might be having anactive chat session with a brand and the session context manager detectsdissatisfaction with the call due to negative sentiment 1731. The chatsession is escalated to a voice-based callback to mitigate the negativeimpact of the dissatisfying chat session 1732. In a consumer event-basedcallback scenario, a consumer takes some action which generates acallback event such as making an in-person purchase and consenting to acallback at a later time 1740; getting lost on the way to a store,wherein the navigation system detects the off-route driving and offers acallback 1741; communicating with a virtual assistant such as Siri orAlexa, wherein the forwarding of the interaction from the bot interfacemanager 740 leads to a callback 1742; or a consumer passes by a nearfield communication (NFC) beacon on a physical display at a store, andconsents to a callback sent to the consumer's mobile device by thebeacon 1743. In each of these circumstances, a callback is scheduledusing the callback scheduler 1744. In a rule-based callback scenario,some pre-established rule triggers a callback such as when a desiredproduct is in stock 1750, when a desired agent is available 1751, orwhen the consumer's position in a physical queue (e.g., at a restaurantthat operates a queue notification system) has advanced to the front ofthe queue 1752. In each of these circumstances, a callback is schedulesusing the callback scheduler. The above-listed examples are not intendedto be limiting, and many other such use cases may be handled by theplatform.

FIG. 18 is a block diagram illustrating an exemplary system architecturefor an adaptive cloud conversation platform connected to brand endpointscomprising contact centers. The exemplary system architecture shown hereis the same as that shown for FIG. 1 , but with the addition of a surveymanager 1900. In this embodiment, survey manager 1900 is a component ofuser management layer 130 of adaptive cloud conversation platform 100and, as such, is capable of communicating with other components ofadaptive cloud conversation platform 100, as has been previouslydescribed with respect to other components such as conversation manager400, session manager 500, event manager 600, etc. Survey manager 1900determines when and how feedback from consumers should be requested inthe form of consumer responses to surveys and determines when and howsuch feedback should result in downstream actions such as notificationsto a brand manager, escalation to a supervising agent, or scheduling ofa callback to the consumer. Survey manager 1900 may use machine learningalgorithms to dynamically adapt survey strategies to meet certainfitness criteria such as improving the likelihood of obtaining aresponse, improving the quality of responses, or reducing consumerfrustration. Such strategies may include, but are not limited to,changing the type of survey performed; changing the communicationchannel(s) through which the survey is performed; changing the time ofperformance of, or the time of invitation to, the survey; changing thequestions asked in the survey; changing the order of the questions; andchanging the wording of the questions. Surveys may be stored in adatabase such as brand environment database 1050.

FIG. 19 is a block diagram illustrating an exemplary system architecturefor a survey manager aspect of an adaptive cloud conversation platform.In this embodiment, survey manager 1900 is a component of usermanagement layer 130 of adaptive cloud conversation platform 100 and, assuch, is capable of communicating with other components of adaptivecloud conversation platform 100, as has been previously described withrespect to other components such as conversation manager 400, sessionmanager 500, event manager 600, etc. Survey manager 1900 of thisembodiment comprises a consumer satisfaction analyzer 1910, a surveycontext manager 1920, an event thrower 1930, and an environment managerinterface 1940.

Survey manager 1900 determines when and how feedback from consumersshould be requested in the form of surveys and determines when and howsuch feedback should result in downstream actions such as notificationsto a brand manager or escalation to a supervising agent. Survey manager1900 uses machine learning algorithms to dynamically adapt surveystrategies to meet certain fitness criteria such as improving thelikelihood of obtaining a response, improving the quality of responses,or reducing consumer frustration. Such strategies may include, but arenot limited to, changing the type of survey performed; changing thecommunication channel(s) through which the survey is performed; changingthe time of performance of, or the time of invitation to, the survey;changing the questions asked in the survey; changing the order of thequestions; and changing the wording of the questions.

Surveys may be of many types including, but not limited to,conversational short message service (SMS) surveys in which questionsare sent to the consumer via SMS and the consumer responds via SMS; webSMS surveys in which an invitation to complete a survey and a link tothe survey are sent via SMS and the consumer responds by clicking on thelink and completing a web-based survey; phone surveys in which thesurvey takes place via an audio call between the consumer and an agent;chat-based surveys in which the survey takes place via a text chatbetween the consumer and an agent, or between the consumer and achatbot; email surveys in which an email is sent to the consumer eitherwith questions to which the consumer can respond by reply email or witha link to a web-based survey; interactive voice response (IVR) surveysin which an outbound call is made to a consumer by an IVR or an inboundconsumer's call is routed to an IVR configured to provide surveyquestions to the consumer via audio and to receive answers from theconsumer via the phone keypad (e.g., dual-tone multi-frequency (DTMF)signals) or by speech from the consumer processed through speech-to-text(STT) processing; stand-alone web surveys to which the consumer may beconnected after a website interaction such as completing a purchaseonline; surveys using application programming interfaces (APIs) toprovide surveys through a standardized interface that can be integratedinto websites, applications, and user interfaces.

Consumer satisfaction analyzer 1910 determines a level of consumersatisfaction. In this embodiment, consumer satisfaction analyzer 1910received goals and intents data from conversation manager 400 andsentiment data either from session manager 500 or from stored sentimentdata from prior interactions. The sentiment data provides a directindication of satisfaction related to a session, a conversation, aproduct, overall satisfaction with a brand, or other aspect of aconsumer-brand relationship. The sentiment data may be compared with thegoals and intents data to obtain a further indirect indication ofsatisfaction with the session, conversation, product, brand, or otheraspect of a consumer-brand relationship. This can provide additionalgranularity in the determination of a consumer's satisfaction. Forexample, sentiment is typically analyzed on a discrete three-level scale(e.g., negative, neutral, or positive). If the consumer's sentiment dataindicates a positive sentiment, this is a good indicator, but provideslittle additional information. However, if conversation manager 400determines that the consumer's goal was to obtain repair of a product,and the brand replaced the product with a new one instead, it can beassumed that the brand exceeded the consumer's goal and a modifier canbe added to the sentiment, such as “high” positive sentiment.Conversely, if conversation manager 400 determines that the consumer'sgoal was to obtain replacement of a product, and the brand repaired theproduct instead, it can be assumed that the brand fell short of theconsumer's goal and a modifier can be added to the sentiment, such as“low” positive sentiment. Likewise, consumer satisfaction analyzer 1910may receive fitness parameters from environment manager 1000 viaenvironment manager interface. The consumer's determined satisfaction(e.g., “low” positive sentiment) can be further compared against fitnessparameters (long-term consumer loyalty) to determine thresholds (brand'sgoal is minimum of “moderate” positive sentiment) for taking action suchas requesting a survey from the consumer.

Survey context manager 1920 receives a determination of satisfactionfrom consumer satisfaction analyzer 1910 along with information fromconsumer profile database 820 such as the consumer profile, any relevantconversation data, and any previously obtained survey data, andrecommends a strategy for surveying consumers based on the receiveddata. The received data is processed by survey strategy analyzer 1921 todetermine whether, when, and how to conduct a survey with the consumerto obtain relevant information about the consumer's satisfaction. Actiongenerator 1922 generates recommended actions based on the outputs fromsurvey strategy analyzer 1921. Survey context manager 1920 learns aboutthe consumer's consumer potential responses to surveys by analyzing datasuch as satisfaction data produced by the consumer satisfaction analyzer1910, information from the consumer's profile such as communicationchannel preferences and time of contact preferences, current and priorconversation data, and prior survey feedback data. Survey contextmanager 1920 uses machine learning algorithms to dynamically adaptsurvey strategies to meet certain fitness criteria such as improving thelikelihood of obtaining a response, improving the quality of responses,or reducing consumer frustration. Such strategies may include, but arenot limited to, changing the type of survey performed; changing thecommunication channel(s) through which the survey is performed; changingthe time of performance of, or the time of invitation to, the survey;changing the questions asked in the survey; changing the order of thequestions; and changing the wording of the questions. The training andoperation of the machine learning algorithm is described later herein.The output of survey context manager 1920 may be fed to other componentsfor actions to be taken (e.g., scheduling of a survey to determine aconsumer's current satisfaction after a period of little or no contactfrom the consumer) or may be fed to the machine learning algorithms ofother components for further analysis (e.g., to the conversation managerto determine the best mode of starting a conversation or session toimplement the survey). In this way, the outputs of the machine learningalgorithm(s) of each component of the platform may be acted onseparately, or may be used as part of a network of machine learningalgorithms, or some combination of the two.

Event thrower 1930 throws events to other platform components withinstructions to schedule or establish communications with a consumereither associated with an existing conversation or a new conversation.Further, event thrower 1930 passes events related to downstream actionsto environment manager interface for handling by environment manager1000.

Environment manager interface 1940 receives fitness parameters fromenvironment manager 1000 and passes them to consumer satisfactionanalyzer 1910. Environment manager interface 1940 further receivesevents thrown by event thrower 1930 related to downstream actionsgenerated by action generator 1922 and passes them to environmentmanager 1000. Downstream events are actions to be taken by brand such assending notifications to a team or group at brand (e.g., via groupmessaging systems such as Slack, Microsoft® Teams, or brand-proprietaryequivalents), sending notifications using native tools in an underlyingconsumer service (e.g., contact center as a service (CCAAS) platforms);creating a service request in a customer relationship management (CRM)platform; and notifying supervisors of managers via SMS, email, or othermeans.

FIG. 20 is a block diagram illustrating an exemplary system architecturefor a machine learning algorithm network aspect of an adaptive cloudconversation platform. The exemplary system architecture shown here isthe same as that shown in FIG. 11 , with the addition of a surveycontext manager 1920. In this embodiment, the survey context manager1920 sentiment analyses (Session CM outputs) as a pass-through fromconversation context manager 420 and goals/needs analyses (ConversationCM outputs) from conversation context manager 420, as well as fitnessparameters from environment manager 1000. Survey context manager 1920determines a level of consumer satisfaction from the sentiment analysesand goals/needs analyses, and uses machine learning algorithms todynamically adapt survey strategies to the fitness criteria such asimproving the likelihood of obtaining a response, improving the qualityof responses, or reducing consumer frustration. Such strategies mayinclude, but are not limited to, changing the type of survey performed;changing the communication channel(s) through which the survey isperformed; changing the time of performance of, or the time ofinvitation to, the survey; changing the questions asked in the survey;changing the order of the questions; and changing the wording of thequestions. The output of survey context manager 1920 may be fed to othercomponents for actions to be taken (e.g., scheduling of a survey todetermine a consumer's current satisfaction after a period of little orno contact from the consumer) or may be fed to the machine learningalgorithms of other components for further analysis (e.g., to theconversation manager to determine the best mode of starting aconversation or session to implement the survey). In this way, theoutputs of the machine learning algorithm(s) of each component of theplatform may be acted on separately, or may be used as part of a networkof machine learning algorithms, or some combination of the two.

The training and operation of the machine learning algorithm isdescribed later herein. In some embodiments, the survey strategies maybe generated from unsupervised machine learning algorithms orreinforcement machine learning algorithms rather than supervised (i.e.,pre-trained) machine learning algorithms. An unsupervised machinelearning algorithm learns from the data itself by association,clustering, or dimensionality reduction, rather than having beenpre-trained to discriminate between labeled input data. Reinforcementlearning algorithms learn from repeated iterations of outcomes based onprobabilities with successful outcomes being rewarded. These types ofmachine learning algorithms are ideal for exploring large number ofpossible outcomes such as possible outcomes from different approaches toa conversation, and so would be suitable for proposing responses toconsumer queries.

FIG. 21 is a flow diagram illustrating an exemplary method for trainingand operation of machine learning algorithms for survey context manager.In this example, it is assumed that a supervised machine learningalgorithm (MLA) is being used, but in other embodiments, unsupervisedmachine learning algorithms or reinforcement machine learning algorithmsmay be used if better suited to the analyses being performed. Here,machine learning algorithm is trained 2110 on labeled data such assatisfaction data 2111 a, consumer profile information 2111 b,conversation and session data 2111 c, and prior survey information 2111n. Once MLA has been trained, actual data is processed 2120 throughtrained machine learning algorithm such as satisfaction data 2121 a fromconsumer satisfaction analyzer 2110, consumer profile data 2121 b, andconversation and session data 2121 c, and prior survey data 2111 n. MLAoutputs a strategy for conducting the survey 2130, and an action istaken based on the strategy 2140 such as scheduling a survey 2141 a,changing the survey type 2141 b, changing the channel of communication2141 c, changing the questions asked 2141 n, etc. As feedback data isreceived from the callback 2150, it may be used to retrain the machinelearning algorithm 2160. Over time, the MLA will adapt its outputs tothe retraining based on real-world data to provide more accuratepredictions.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (“ASIC”), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 22 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one embodiment, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one embodiment, a computing device 10 may beconfigured or designed to function as a server system utilizing CPU 12,local memory 11 and/or remote memory 16, and interface(s) 15. In atleast one embodiment, CPU 12 may be caused to perform one or more of thedifferent types of functions and/or operations under the control ofsoftware modules or components, which for example, may include anoperating system and any appropriate applications software, drivers, andthe like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some embodiments, processors 13 may includespecially designed hardware such as application-specific integratedcircuits (ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a specific embodiment,a local memory 11 (such as non-volatile random access memory (RAM)and/or read-only memory (ROM), including for example one or more levelsof cached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one embodiment, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity AN hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 22 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe inventions described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one embodiment, a single processor 13 handles communicationsas well as routing computations, while in other embodiments a separatededicated communications processor may be provided. In variousembodiments, different types of features or functionalities may beimplemented in a system according to the invention that includes aclient device (such as a tablet device or smartphone running clientsoftware) and server systems (such as a server system described in moredetail below).

Regardless of network device configuration, the system of the presentinvention may employ one or more memories or memory modules (such as,for example, remote memory block 16 and local memory 11) configured tostore data, program instructions for the general-purpose networkoperations, or other information relating to the functionality of theembodiments described herein (or any combinations of the above). Programinstructions may control execution of or comprise an operating systemand/or one or more applications, for example. Memory 16 or memories 11,16 may also be configured to store data structures, configuration data,encryption data, historical system operations information, or any otherspecific or generic non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device embodiments may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some embodiments, systems according to the present invention may beimplemented on a standalone computing system. Referring now to FIG. 23 ,there is shown a block diagram depicting a typical exemplaryarchitecture of one or more embodiments or components thereof on astandalone computing system. Computing device 20 includes processors 21that may run software that carry out one or more functions orapplications of embodiments of the invention, such as for example aclient application 24. Processors 21 may carry out computinginstructions under control of an operating system 22 such as, forexample, a version of MICROSOFT WINDOWS™ operating system, APPLE OSX™ oriOS™ operating systems, some variety of the Linux operating system,ANDROID™ operating system, or the like. In many cases, one or moreshared services 23 may be operable in system 20, and may be useful forproviding common services to client applications 24. Services 23 may forexample be WINDOWS™ services, user-space common services in a Linuxenvironment, or any other type of common service architecture used withoperating system 21. Input devices 28 may be of any type suitable forreceiving user input, including for example a keyboard, touchscreen,microphone (for example, for voice input), mouse, touchpad, trackball,or any combination thereof. Output devices 27 may be of any typesuitable for providing output to one or more users, whether remote orlocal to system 20, and may include for example one or more screens forvisual output, speakers, printers, or any combination thereof. Memory 25may be random-access memory having any structure and architecture knownin the art, for use by processors 21, for example to run software.Storage devices 26 may be any magnetic, optical, mechanical, memristor,or electrical storage device for storage of data in digital form (suchas those described above, referring to FIG. 22 ). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some embodiments, systems of the present invention may be implementedon a distributed computing network, such as one having any number ofclients and/or servers. Referring now to FIG. 24 , there is shown ablock diagram depicting an exemplary architecture 30 for implementing atleast a portion of a system according to an embodiment of the inventionon a distributed computing network. According to the embodiment, anynumber of clients 33 may be provided. Each client 33 may run softwarefor implementing client-side portions of the present invention; clientsmay comprise a system 20 such as that illustrated in FIG. 23 . Inaddition, any number of servers 32 may be provided for handling requestsreceived from one or more clients 33. Clients 33 and servers 32 maycommunicate with one another via one or more electronic networks 31,which may be in various embodiments any of the Internet, a wide areanetwork, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the invention does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some embodiments, servers 32 may call external services37 when needed to obtain additional information, or to refer toadditional data concerning a particular call. Communications withexternal services 37 may take place, for example, via one or morenetworks 31. In various embodiments, external services 37 may compriseweb-enabled services or functionality related to or installed on thehardware device itself. For example, in an embodiment where clientapplications 24 are implemented on a smartphone or other electronicdevice, client applications 24 may obtain information stored in a serversystem 32 in the cloud or on an external service 37 deployed on one ormore of a particular enterprise's or user's premises.

In some embodiments of the invention, clients 33 or servers 32 (or both)may make use of one or more specialized services or appliances that maybe deployed locally or remotely across one or more networks 31. Forexample, one or more databases 34 may be used or referred to by one ormore embodiments of the invention. It should be understood by one havingordinary skill in the art that databases 34 may be arranged in a widevariety of architectures and using a wide variety of data access andmanipulation means. For example, in various embodiments one or moredatabases 34 may comprise a relational database system using astructured query language (SQL), while others may comprise analternative data storage technology such as those referred to in the artas “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and soforth). In some embodiments, variant database architectures such ascolumn-oriented databases, in-memory databases, clustered databases,distributed databases, or even flat file data repositories may be usedaccording to the invention. It will be appreciated by one havingordinary skill in the art that any combination of known or futuredatabase technologies may be used as appropriate, unless a specificdatabase technology or a specific arrangement of components is specifiedfor a particular embodiment herein. Moreover, it should be appreciatedthat the term “database” as used herein may refer to a physical databasemachine, a cluster of machines acting as a single database system, or alogical database within an overall database management system. Unless aspecific meaning is specified for a given use of the term “database”, itshould be construed to mean any of these senses of the word, all ofwhich are understood as a plain meaning of the term “database” by thosehaving ordinary skill in the art.

Similarly, most embodiments of the invention may make use of one or moresecurity systems 36 and configuration systems 35. Security andconfiguration management are common information technology (IT) and webfunctions, and some amount of each are generally associated with any ITor web systems. It should be understood by one having ordinary skill inthe art that any configuration or security subsystems known in the artnow or in the future may be used in conjunction with embodiments of theinvention without limitation, unless a specific security 36 orconfiguration system 35 or approach is specifically required by thedescription of any specific embodiment.

FIG. 25 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to keyboard 49, pointing device 50,hard disk 52, and real-time clock 51. NIC 53 connects to network 54,which may be the Internet or a local network, which local network may ormay not have connections to the Internet. Also shown as part of system40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various embodiments, functionality for implementing systems ormethods of the present invention may be distributed among any number ofclient and/or server components. For example, various software modulesmay be implemented for performing various functions in connection withthe present invention, and such modules may be variously implemented torun on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various embodiments described above. Accordingly, the presentinvention is defined by the claims and their equivalents.

What is claimed is:
 1. An adaptive cloud conversation platform, comprising: a computing device comprising a memory, a processor, and a non-volatile data storage device; a consumer profile database stored on the non-volatile data storage device, the consumer profile database comprising one or more consumer profiles; a survey manager comprising a first plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive conversation data for a conversation with a consumer, the conversation data comprising an interaction between the consumer and a brand and satisfaction data for the conversation; process the conversation data through a first machine learning algorithm to obtain a survey strategy, the survey strategy comprising a determination that a survey of the consumer should be conducted and a type of survey to be conducted; and forward the survey strategy to a conversation manager; the conversation manager comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the survey strategy; retrieve a consumer profile for the consumer from the consumer profile database, the consumer profile comprising a plurality of preferences of the consumer; process the plurality of preferences through a second machine learning algorithm to select a channel through which to conduct the survey with the consumer; and forward the channel selection to a schedule manager; the schedule manager comprising a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the channel selection from the conversation manager; retrieve the consumer profile; process the plurality of preferences through a third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; schedule a survey to be conducted with the consumer at the selected time through the selected channel; and forward the survey schedule to a callback manager; and the callback manager comprising a fourth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the survey schedule; and retrieve a survey of the type specified in the survey strategy; execute the survey strategy by conducting the survey at the selected time through the selected channel as indicated in the survey schedule.
 2. The platform of claim 1, wherein: the survey manager is further configured to: receive survey feedback from the consumer; process the survey feedback through the first machine learning algorithm to determine whether a callback is recommended based on the survey feedback; and forward the determination to the conversation manager; the conversation manager is further configured to: receive the determination; retrieve the consumer profile; process the plurality of preferences through the second machine learning algorithm to select a channel through which to conduct the callback with the consumer; the schedule manager is further configured to: receive the channel selection from the conversation manager; retrieve the consumer profile; process the plurality of preferences through the third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; schedule a callback to be conducted with the consumer at the selected time through the selected channel; and forward the survey schedule to the callback manager; and the callback manager is further configured to: receive the callback schedule; and execute the callback by conducting the callback at the selected time through the selected channel as indicated in the callback schedule.
 3. The platform of claim 1, further comprising a consumer context manager comprising a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the conversation data; retrieve the consumer profile, the consumer profile further comprising a plurality of behaviors of the consumer; process the conversation data and the plurality of behaviors of the consumer through a fourth machine learning algorithm to determine whether a second callback to the consumer should be made; and where the determination is that a second callback should be made, forward the determination to the conversation manager as the determination that a callback should be made to a consumer.
 4. The platform of claim 3, further comprising a session manager comprising a fourth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the conversation data; process the conversation data through a fifth machine learning algorithm to determine a consumer sentiment; and forward the determined sentiment to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made.
 5. The platform of claim 3, wherein: the conversation manager is further configured to: process the conversation data through a sixth machine learning algorithm to determine a consumer goal, need, or intent; and forward the determined goal, need, or intent to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made.
 6. The platform of claim 3, further comprising: an event rules database stored on the non-volatile data storage device, the event rules database comprising rules for triggering communications with consumers based on events occurring outside of a conversation; and an event manager comprising a seventh plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive notification of an event; match the event to a rule in the event rules database; and forward the matched event to the conversation manager as the determination that a callback should be made to the consumer.
 7. The platform of claim 6, wherein: the event manager is further configured to: retrieve the consumer profile; process the consumer profile and one or more rules from the event rules database through an eighth machine learning algorithm to determine a new rule for triggering communications with the consumer; and store the new rule in the event rules database.
 8. The platform of claim 3, further comprising: a brand environment database stored on the non-volatile data storage device, the brand environment database comprising brand information related to conversations with consumers of the brand; an environment manager comprising an eighth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: retrieve the consumer profile; retrieve the brand information from the brand environment database; process the consumer profile and the brand information through a ninth machine learning algorithm to determine whether a campaign of communications should be established with a plurality of consumers; and forward the determination to the conversation manager as the determination that a callback should be made to the consumer.
 9. The platform of claim 8, further comprising: a consumer manager comprising a ninth plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: retrieve the consumer profile for the consumer from the consumer profile database; receive a fitness parameter from the environment manager; process the consumer profile and the fitness parameter through a tenth machine learning algorithm to identify opportunities for proactive conversations with the consumer; and forward the identified to the consumer context manager as an additional input to the second machine learning algorithm's selection of the channel through which the callback should be made.
 10. A method for operating an adaptive cloud conversation platform, comprising the steps of: using a survey manager operating on a computing device comprising a memory, a processor, and a non-volatile data storage device to: receive conversation data for a conversation with a consumer, the conversation data comprising an interaction between the consumer and a brand and satisfaction data for the conversation; process the conversation data through a first machine learning algorithm to obtain a survey strategy, the survey strategy comprising a determination that a survey of the consumer should be conducted and a type of survey to be conducted; and forward the survey strategy to a conversation manager; a conversation manager comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the survey strategy; retrieve a consumer profile for the consumer from a consumer profile database stored on the non-volatile data storage device, the consumer profile database comprising one or more consumer profiles, and the consumer profile comprising a plurality of preferences of the consumer; process the plurality of preferences through a second machine learning algorithm to select a channel through which to conduct the survey with the consumer; and forward the channel selection to a schedule manager; using a schedule manager operating on the computing device to: receive the channel selection from the conversation manager; retrieve the consumer profile; process the plurality of preferences through a third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; schedule a survey to be conducted with the consumer at the selected time through the selected channel; and forward the survey schedule to a callback manager; and using a callback manager operating on the computing device to: receive the survey schedule; and retrieve a survey of the type specified in the survey strategy; execute the survey strategy by conducting the survey at the selected time through the selected channel as indicated in the survey schedule.
 11. The method of claim 10, further comprising the steps of: using the survey manager to: receive survey feedback from the consumer; process the survey feedback through the first machine learning algorithm to determine whether a callback is recommended based on the survey feedback; and forward the determination to the conversation manager; using the conversation manager to: receive the determination; retrieve the consumer profile; process the plurality of preferences through the second machine learning algorithm to select a channel through which to conduct the callback with the consumer; using the schedule manager to: receive the channel selection from the conversation manager; retrieve the consumer profile; process the plurality of preferences through the third machine learning algorithm to select a time at which to conduct the callback with the consumer through the selected channel; schedule a callback to be conducted with the consumer at the selected time through the selected channel; and forward the survey schedule to the callback manager; and using the callback manager to: receive the callback schedule; and execute the callback by conducting the callback at the selected time through the selected channel as indicated in the callback schedule.
 12. The method of claim 10, further comprising the steps of using a consumer context manager operating on the computing device to: receive the conversation data; retrieve the consumer profile, the consumer profile further comprising a plurality of behaviors of the consumer; process the conversation data and the plurality of behaviors of the consumer through a fourth machine learning algorithm to determine whether a second callback to the consumer should be made; and where the determination is that a second callback should be made, forward the determination to the conversation manager as the determination that a callback should be made to a consumer.
 13. The method of claim 12, further comprising the steps of using a session manager operating on the computing device to: receive the conversation data; process the conversation data through a fifth machine learning algorithm to determine a consumer sentiment; and forward the determined sentiment to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made.
 14. The method of claim 12, further comprising the steps of: using the conversation manager to: process the conversation data through a sixth machine learning algorithm to determine a consumer goal, need, or intent; and forward the determined goal, need, or intent to the consumer context manager as an additional input to the fourth machine learning algorithm's determination as to whether a callback to the consumer should be made.
 15. The method of claim 12, further comprising the steps of: creating an event rules database on the non-volatile data storage device, the event rules database comprising rules for triggering communications with consumers based on events occurring outside of a conversation; and using an event manager operating on the computing device to: receive notification of an event; match the event to a rule in the event rules database; and forward the matched event to the conversation manager as the determination that a callback should be made to the consumer.
 16. The method of claim 15, further comprising the steps of: using the event manager to: retrieve the consumer profile; process the consumer profile and one or more rules from the event rules database through an eighth machine learning algorithm to determine a new rule for triggering communications with the consumer; and store the new rule in the event rules database.
 17. The method of claim 12, further comprising the steps of: creating a brand environment database on the non-volatile data storage device, the brand environment database comprising brand information related to conversations with consumers of the brand; using an environment manager operating on the computing device to: retrieve the consumer profile; retrieve the brand information from the brand environment database; process the consumer profile and the brand information through a ninth machine learning algorithm to determine whether a campaign of communications should be established with a plurality of consumers; and forward the determination to the conversation manager as the determination that a callback should be made to the consumer.
 18. The method of claim 17, further comprising the steps of: using a consumer manager operating on the computing device to: retrieve the consumer profile for the consumer from the consumer profile database; receive a fitness parameter from the environment manager; process the consumer profile and the fitness parameter through a tenth machine learning algorithm to identify opportunities for proactive conversations with the consumer; and forward the identified to the consumer context manager as an additional input to the second machine learning algorithm's selection of the channel through which the callback should be made. 